FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains

Abstract

The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc.

Keywords: QTAIM; atomic charge; force field; machine learning; peptides; quantum chemical topology; transferability.